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Difficulty of Interruptions 1 


Does the Difficulty of an Interruption Affect our Ability to Resume? 


David M. Cades 
George Mason University 

Deborah A. Boehm Davis 
George Mason University 


J. Gregory Trafton 
Naval Research Laboratory 

Christopher A. Mo nk 
George Mason University 


Research has shown that different types of interruptions can affect their disruptiveness. 
However, it is unclear how different features of the interrupting task determine its 
disruptive effects. Specifically, some theories predict that the difficulty of an interruption 
does not contribute to the disruptive effects of that interruption alone. Disruptive effects 
can be mediated by the extent to which the interrupting task interferes with the ability to 
rehearse during the interruption. In this experiment participants performed a single 
primary task with three interruptions of different difficulty. We found that interruptions 
were more disruptive when the task minimized the participant’s ability to rehearse (as 
measured by the number of mental operators required to perform the task) and not just 
when they were more difficult. These results suggest that the ability to rehearse during an 
interruption is critical in facilitating resumption of a primary task. 


INTRODUCTION 

As we are faced with more and more sources of 
information vying for our attention at any given 
time, it is becoming increasingly important to 
understand how interruptions affect our abilities to 
complete tasks. Not surprisingly, many studies of 
the effects of interruptions have shown them to be 
disruptive to the performance of a primary task 
(Gillie & Broadbent, 1989; Miyata & Nonnan, 

1986; Monk, 2004; Trafton, Altmann, Brock, & 
Mintz, 2003). However, little is known about the 
role played by the various features of interrupting 
tasks such as modality, similarity, or difficulty. This 
paper seeks to examine the difficulty aspect in order 
to better understand how this aspect makes 
interruptions more or less disruptive. 

Although no comprehensive theory of 
interrupted task perfonnance currently exists, the 
goal-activation model (Altmann & Trafton, 2002) 
does make predictions of how disruptive an 
interruption will be. Simply, the model suggests that 
the disruption will be greater the longer an 
interruption is and the less a person rehearses the 
primary task during the interruption. The model, 
however, does not make any specific predictions 


related to how other features of an interrupting task 
such as similarity and difficulty, will affect 
disruptiveness beyond how those aspects interact 
with interruption duration and rehearsal. Other 
research on interruptions has suggested that 
disruptiveness is directly related to the difficulty of 
the interruption, regardless of whether a person has 
the opportunity to rehearse (Gillie & Broadbent, 
1989). It is important to note that as the mental 
complexity, or amount of mental effort, of the 
secondary task increases, the opportunity to 
rehearse the primary task decreases. 

This paper examines the extent to which 
difficulty of the interrupting task disrupts primary 
task performance, with a particular focus on the role 
that opportunity for rehearsal play in detennining 
the disruptiveness of the interruption. 

EXPERIMENT 

In order to examine how the difficulty of an 
interruption, presumably through its interference 
with the opportunity to rehearse, affects the 
resumption of a task, we conducted an experiment 
in which participants perfonned a primary task with 
interruptions of three levels of difficulty. In this 



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Does the Difficulty of an Interruption Affect our Ability to Resume? 

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Naval Research Laboratory,Navy Center for Applied Research in 

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Human Factors and Ergonomics Society 51st Annual Meeting, Oct 1-5, 2007, Baltimore, MD 

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Difficulty of Interruptions 2 


experiment, all participants performed three 
sessions of a primary task with interruptions. In one 
condition they were interrupted with a simple 
shadowing task, in which they repeated numbers 
aloud read to them by the computer. In the other 
two interruption conditions, they were interrupted 
with variations of the n-back working memory task 
(Lovett, Daily, & Reder, 2000). Both n-back tasks 
required participants to listen to a series of numbers 
read aloud by the computer, to make judgments as 
to whether or not the most recently read number 
was higher or lower than one of the previously read 
numbers and, finally, to acknowledge their choice 
by clicking on either a “Higher” or “Lower” button 
located at the top of the screen. In the easy (1-back) 
task, they were asked to compare the most recent 
number to the one just before it and in the difficult 
(3-back task), they were asked to compare the most 
recent number to the one three numbers prior. 

COGNITIVE TASK ANALYSIS 

On the surface, it would appear that the 3-back 
task was more difficult than the 1-back task, which 
in turn was more difficult than the shadowing task. 
This surface appearance is based on the fact that it 
should be easier to remember and compare two 
number read consecutively (1-back) than to 
compare a two numbers separated by two other 
numbers (3-back), and that both of these tasks 
should be easier than simply repeating a number 
(shadowing). However, in order to understand 
whether each of these tasks might allow for 
rehearsal it is important to look at the resources that 
each requires. 

An NGOMSL (Natural Language Goals, 
Operators, Methods, Selection Rules) (Kieras & 
Poison, 1985) task analysis was performed on the 
three interruption tasks (see Table 1) to determine 
the likelihood that participants could rehearse while 
performing that task. Both the 1-back and 3-back 
tasks were found to have three mental operators, 
while the shadowing task had zero. The presence of 
the mental operators in the 1-back and 3-back tasks 
would suggest that participants needed to maintain 
information in memory, which would likely reduce 
rehearsal ability during these interruptions. The lack 
of any mental operators in the analysis of the 


shadowing task suggests that participants did have 
at least the opportunity to rehearse during the 
interruption. 

Task/Actions Operator 

1-Back 


Table 1: NGOMSL analysis of Shadowing, 1-Back, and 
3-Back interrupting tasks 


Listen to 1st Number 

Perceive 

Listen to 2nd Number 

Perceive 

Remember 1st Number 

Mental 

Compare 2 Numbers 

Mental 

Decide if 2nd Number is Higher or 

Mental 

Lower 


Move mouse to proper button 

Point 

Click Button 

Click 

3-Back 


Listen to 1st Number 

Perceive 

Listen to 2nd Number 

Perceive 

Listen to 3rd Number 

Perceive 

Listen to 4th Number 

Perceive 

Remember 1st Number 

Mental 

Compare 1st and 4th Numbers 

Mental 

Decide if 4th Number is Higher or Lower 

Mental 

Move mouse to proper button 

Point 

Click Button 

Click 

Shadowinq 


Listen to 1st Number 

Perceive 

Say 1st Number 



Thus, if the opportunity to rehearse has a direct 
impact on people’s ability to resume a task 
following an interruption, as is predicted by the 
memory for goals model (Altmann & Trafton, 
2002), the NGOMSL analysis suggests that the 1- 
back and 3-back tasks would show more disruptive 
effects than the shadowing task. In other words, 
people would resume fastest in the shadowing 
condition and slower in the two n-back conditions. 
Alternatively, if difficulty of the interrupting task 
has a direct role in detennining the interruption’s 
disruptiveness, we expect that the shadowing task 
would be the least disruptive, followed by the 1- 
back task, and then the 3-back task. Specifically, 
participants would resume fastest in the shadowing 
condition, slower in the 1-back condition, and 
slowest in the 3-back condition. 


METHOD 



Difficulty of Interruptions 3 


Participants 

Thirty-six undergraduates from George Mason 
University participated in this experiment for class 
credit. All were randomly assigned to either the 
shadowing, the easy //-back, or the difficult //-back 
interruption condition. 

Task and Materials 

The primary task (see Figure 1) consisted of 
programming a Video Cassette Recorder (VCR) 
interface to record a specific television program in 
the future. The interruption tasks consisted of a 
simple number shadowing task (the easiest 
condition) and two variations of the //-back working 
memory task (Lovett et ah, 2000), a 1-back task 
(the medium difficulty condition) and a 3-back task 
(the hardest condition). The VCR interface was 
programmed in Macintosh Common Lisp, was 
designed for experimental use, and was not based 
on any specific VCR model (Gray, 2000). In order 
to program a show on the VCR, participants were 
given a 3x5 index card with the name, start time, 
end time, day of the week, and channel of a 
television program. The programming task was 
completed once all of this information was entered 
into the computer. During all interruption tasks, 
numbers ranging from one to nine were read aloud 
by the computer at a rate of one number every three 
seconds. Each interruption lasted for thirty seconds. 
Participants were interrupted an average of eleven 
times per session. 

Design and Procedure 

The experiment was a 3 x 3 mixed factorial 
design with interruption difficulty as a between 
subjects factor with three levels (Shadowing, 1- 
back, and 3-back) and sessions as a within subjects 
factor with three levels (1,2, and 3). Participants 
were trained on the VCR task individually and then 
the VCR task with either the shadowing, 1-back, or 
3-back interruption depending on what condition 
they were in. Participants then completed three 
sessions, with each session consisting of three 
different television shows to program. Each session 
lasted approximately fifteen minutes and contained 



Figure 1: The VCR Interface 

an average of eleven 30 second interruptions. 
Interruptions were triggered by a random number of 
mouse clicks on the VCR task ranging from fifteen 
to twenty two; however this was not apparent to the 
participants. At the onset of the interruption, the 
VCR interface would disappear and a new screen 
would be presented with the “Higher” and “Lower” 
buttons on the top. After thirty seconds this screen 
would disappear and the VCR interface would 
return. A short break was given between each 
session. 

Measures 

Each mouse action was time-stamped and 
recorded for all participants. The inter-action 
interval represents the amount of time between any 
two actions on the primary VCR task. A resumption 
lag is a special type of inter-action interval taken by 
measuring the action time between the ending of the 
interrupting task and the first action back on the 
primary task. This measure (Altmann & Trafton, 
2002) has been used accurately to quantify the 
disruptive effects of interruptions in the past (Monk, 
2004; Trafton et ah, 2003). 

RESULTS AND DISCUSSION 

Resumption lags below 200 milliseconds were 
removed from the data because they likely were 
anticipatory clicks resulting from the fact that the //- 
back interrupting tasks required participants to click 
very close to the time when the interruption ended 
and the VCR task reappeared on the screen. 
Following this, outliers greater than three standard 














Difficulty of Interruptions 4 


deviations from the mean were removed, which 
constituted 1.1% of the total data. 

The following results suggest that the difficulty 
alone does not dictate how disruptive an 
interruption is. If difficulty alone causes disruption, 
then we would expect the most difficult (3-back) 
condition to show the slowest resumption lags, the 
easy /7-back (1-back) intermediate resumption lags, 
and the easiest (shadowing) condition, the fastest 
resumption lags. A repeated measures ANOVA, 
with interruption difficulty as a between-subjects 
factor, did show a significant main effect for 
interruption difficulty (F( 2, 33) = 7.83, p < .01, 
MSE= 1,062,687, rf = .32). 

However, although participants resumed 
significantly faster in the Easy (shadowing) 
condition than in either the easy //-back (1-back) (p 
< .01) or the hard /7-back (3-back) conditions ip < 
.01), resumption times in the hard /7-back (3-back) 
and easy /7-back (1-back) conditions were not 
statistically different (p = 1.0) based on Tukey HSD 
post hoc comparisons (see Figure 2). Thus, these 
data suggest that while people are disrupted by 
interruptions in all three of these conditions 
(resumption lags are longer than inter-action 
intervals), interruption difficulty can not be the sole 
reason for the disruptiveness. 


■ Hard n-back (3-back) 
□ Easy n-back (1-back) 



Figure 2: Average Resumption Lag by Session by 
interruption Difficulty (Error Bars are Standard Error) 

If difficulty of the task only plays a role to the 
extent that the task prevents rehearsal, as suggested 
by Altmann & Trafton (2002), we might expect a 
different outcome. The NGOMSL analysis 
suggested the 1-back task was sufficiently taxing to 
minimize rehearsal of the primary task during the 


interruption. If this is true, the 3-back task (which 
will also minimize rehearsal) should not lead to 
decreased resumption ability over and above the 1- 
back task according to the memory for goals model. 
The analysis also suggested that the shadowing 
task, with no mental operators, would be more 
likely to allow participants to rehearse. Thus, this 
theory would predict that people should be able to 
resume the primary task better following an 
interruption with the shadowing task. When 
compared to both the 1-back and 3-back conditions, 
the resumption lags in the shadowing condition 
indeed were significantly faster across all three 
sessions, supporting the interpretation that difficulty 
may affect disruptiveness through the prevention of 
rehearsal. 

Additionally, the mean inter-action intervals 
decreased linearly across sessions for all conditions 
(F(l, 34) = 17.95,// < .001, MSE = 87,772, rf = 
.35). This confirmed the practice effect for the 
primary task shown repeatedly in interrupted task 
performance and suggested that any differences in 
resumption perfonnance was not due to an 
interaction between the interruption type and the 
primary task (Cades, Trafton, & Boehm-Davis, 
2006; Trafton et al., 2003). These data also show 
that, as in Cades et al. (2006), participants resumed 
faster with more practice on the interruption (see 
Figure 2), suggesting further that interruption type 
does not affect people’s ability to improve over 
time at dealing with interruptions. When collapsed 
across all conditions, resumption lags decreased 
linearly across sessions (F( \, 33) = 13.59,// < .001, 
MSE = 230,530, rf = .29). 

GENERAL DISCUSSION 

Although we cannot detennine from our data 
whether participants were rehearsing during any of 
the interruption conditions, we can be sure that 
there is more to assessing the disruptiveness of an 
interruption then just examining its difficulty alone. 
Our data showed that simply repeating numbers is 
less disruptive than having to make any type of 
comparison, whether it is one that places a 
relatively low load on working memory or a slightly 
higher one. 


Difficulty of Interruptions 5 


Both the 1-back and 3-back tasks required three 
mental operators. It may be that had we used a 
fourth task that required additional mental 
operators, we would have disrupted resumption 
ability beyond the levels shown here. However, it 
could be that once rehearsal has been minimized it 
really does not matter how much additional burden 
the interrupting task places on the participant. The 
implication of this work is that we cannot simply 
say that more difficult interruptions will lead to 
greater disruptions. Rather, we must consider other 
features of the interruption to gain a full 
understanding of how disruptive a particular task 
will be. 

REFERENCES 

Altmann, E. M., & Trafton, J. G. (2004). Task interruption: 

Resumption lab and the role of cues. Paper presented 
at the Proceedings of the 26th annual conference of 
the Cognitive Science Society. 

Cades, D. M., Trafton, J. G., & Boehm-Davis, D. A. (2006). 

Mitigating disruptions: Can resuming an interrupted 
task be trained? Paper presented at the Proceedings 
of the Human Factors and Ergonomics Society 50 th 
Annual Meeting. 

Gillie, T., & Broadbent, D. (1989). What makes interruptions 
disruptive? A study of length, similarity, and 
complexity. Psychological Research, 50, 243-250. 
Gray, W. D., (2000). The nature and processing of errors in 
interactive behavior. Cognitive Science, 24(2), 205- 
248. 

Kieras, D. E., & Poison, P. G. (1985). An approach to the 
formal analysis of user complexity. International 
Journal of Man-Machine Studies, 22, 365-394. 
Lovett, M. C., Daily, L. Z., & Reder, L. M. (2000). A source 
of activation theory of working memory: Cross-task 
prediction of performance in ACT-R. Journal of 
Cognitive Systems Research, 1, 99-118. 

Miyata, Y., & Norman, D. A. (1986). Psychological issues in 
support of multiple activities. In D. A. N. a. S. W. 
Draper (Ed.), User centered system design (pp. 265- 
284). Hillsdale, NJ: Lawrence Erlbaum Associates 
Inc. 

Monk, C. A. (2004). The effect of frequent versus infrequent 
interruptions on primary task resumption. Paper 
presented at the Proceedings of the Human Factors 
and Ergonomics Society 48th Annual Meeting. 
Trafton, J. G., Altmann, E. M., Brock, D. P., & Mintz, F. E. 
(2003). Preparing to resume an interrupted task: 
Effects of prospective goal encoding and 
retrospective rehearsal. International Journal of 
Human Computer Studies, 55(5), 583-603.